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The Saturnalia Evolution Index is a custom-designed vegetation index computed every time a satellite image on the area is available (best-case scenario every 2-3 days, worst-case every 15 days). The starting point are estimates of reflectance from the vineyard obtained by measuring the amount of sun radiation that reaches the satellite sensor after reflection by vine leaves. Leaf reflectance is linked to various features of the observed plant, even more so its evolution in time and its values at designated observation timeframes. The relevant information is generally contained in a handful of visible and infrared bands. Standard vegetation indexes such as NDVI (Normalized Differential Vegetation Index) typically rely on one visible and one infrared band; with our index we consider several bands, to achieve a more comprehensive understanding of the vine status and trend. The explanation on the following link can help you grasp how reflectance spectra work:
Once the season is over, we condense all recorded SEI values into a single value, called Saturnalia Variation Index. This measure is linked to vine vigour and other quality-related parameters. The SVI provides at a glance an overall idea of the vegetation activity throughout the whole growth season.The values are further grouped in 5 classes and displayed in maps with different colours.
Weather data is collected from several sources. The main source is represented by Earth-observing satellites, whose repositories provide access to weather data worldwide. In some cases we also leveraged weather data collected by ground stations.
Specifically, precipitation data are retrieved from the Global Precipitation Measurement mission, operated by NASA (website). The Land surface temperature - not to be confused with air temperature - is a service operated by Copernicus Land (website). These are just examples, as different geographical areas may pose different challenges, impose different requirements and offer different solutions for data procurement. However, we believe we managed to build a remarkably complete weather data repository on our areas of interest.
Price points are provided to Saturnalia by Liv-ex, the leading global marketplace for fine wine, in the framework of an agreement.
Most of the raw data is collected from producers’ websites. This is done with an intent to use as direct a source as possible, to reduce the possibility of mistake to the minimum. Notwithstanding our efforts, however, errors are not impossible; in case you see one, please help us by contacting firstname.lastname@example.org to report it.
The fair price is computed by our model by considering scores and prices for previous vintages. If the score correlates well with the price, the confidence is high. For example, if the current price is lower than the fair value, it is a good moment for purchasing.
Price prediction is based on a model developed by the Saturnalia team. Based on previous records of price trends and observed effects of vintage releases, the model can explain (and thus forecast) a large fraction of price fluctuations. The model is updated monthly based on fresh incoming data.
The Saturnalia score takes advantage of artificial intelligence (AI) to derive a score based on location, weather and trends of satellite observables. No tasting is involved. The trends recorded during the season are compared with previous vintages in order to provide an assessment in the form of a score in hundreds. As we rely solely on data and AI-driven algorithms, our scores do not include personal opinions or preferences.
The so-called “price per point” is one of the most popular measures used to understand the “value for money” of a specific wine. For example, thanks to it one can attempt a sort of “normalization” that allows a clearer comparison of different wines and vintages in order to understand which ones are more convenient in financial terms.
Of course we cannot get to know what winemakers do in the winery. As our focus is on fine wines, we assume that they'll do the best that they can, given the quality of the collected grapes. On a longer-term perspective, the accumulation of processed records will lead to “implicit modeling” of the winemaker contribution in the AI-based model.
As Alessandro Morichetti said (translated from italian): "A good wine is born in the winery when good grapes meet good knowledge. The first without the second is silent, the second without the first is blind."
Saturnalia gives its users the chance to discover the most famous vineyards in the world through a 3-dimensional model. These maps are built thanks to our thorough research and take advantage of high-resolution digital elevation models. This combination enables unprecedented understanding of the vineyards and the possibility to appreciate every unique feature with just a few clicks. You can pan the territory of the vineyards, zoom in and zoom out and discover for yourself the orography that makes every vineyard so unique.
It took a lot of research and development effort to develop our early scores. We started with Bordeaux wines as it was easier to gather data on them. Moreover, the En Primeur event guarantees scores every year and therefore a continuous assessment and improvement of the model. We are currently working to extend our model to other areas of the world such as Barolo and Burgundy.
Unfortunately there is no straightforward answer to that. That is why we had to design a model to predict the evolution of prices. The model takes into account more variables that help explain the variation in price of previous vintages when a new one is released.
Soil plays a crucial role in grapes growing and therefore in the wine produced. The concept of terroir is a combination of sun exposure, soil and weather. Our early scores do take into consideration the location of the vineyards, and the soil composition, where available. In any case, soil does not change significantly from one year to the next and, even where unknown a priori, the model will eventually “learn” its contribution and increasingly incorporate it into its prediction. The Saturnalia team is committed to add information on soils among the other available datasets.
The uses are multiple. Thanks to our early scores and prediction models, you can decide in advance what to do next with your portfolio, what to sell and what to buy. Our data is released soon after the harvest, therefore you’ll have plenty of time to decide your moves and avoid hasty decisions. If you provide investment services for your customers, our data can help in assembling complete and interactive reports. The Saturnalia platform can help keep track of your portfolio, assess previous performances and fine tune it to maximise your profit. The monthly prediction tool is updated every month.
The Saturnalia harvest reports represent a review of the vintage for a specific area. In particular, we include analysis on weather patterns and comparison with previous vintages on specific appellations. We also identify possible crucial meteorological events that may have influenced the outcome of the vintage. Finally, we point out which areas, based on our data, are likely to perform best.
Our goal is to provide an overview of the vintage that be as objective as possible, and based solely on facts.
Our priorities are Burgundy, Barolo and Bolgheri. They are all key areas for wine investment onto which wine enthusiasts and collectors want to discover more. As satellites know no bounds, our mission will lead us to expand the pool of wines available by including more areas such as Napa and Champagne.
Saturnalia data is made available via a web platform, or via API. For easy integration of our measures into your DB you can leverage our API system: it is fast and plugs in seamlessly (we can also help you do that). In case you just want to take advantage of our data for your offline reports, we have a solution here as well. Please contact us and we will provide you with the necessary data in high-resolution for print and digital press.
The mission of Saturnalia is to share as much objective data as possible on wines, including vintage comparisons and expected scores. Moreover, we are able to share this information soon after the harvest.
However, our role is not meant to replace critics. Their tasting notes cannot be replaced by our scores and they provide useful information to consumers.
Therefore, if you want to add a “geeky twist” to your wine, make sure to subscribe to our service and know every detail.
In our discussion, a satellite is a technological device orbiting around the Earth. There are several types of satellites; for our purposes, we refer only to Earth Observation satellites, meaning devices carrying sensors looking down to the Earth. Many satellites are currently in orbit, both for military and civilian purposes. As a fun fact, although satellite technology has boomed only very recently, the first civilian satellite designed to routinely monitor the planet, was launched as early as 1972. The “Landsat 1” satellite was just the first of a series that continues to the present day.
Recent advancements in satellite technology, combined with huge investments and lower launching costs have boosted the number of satellites in orbit.
Depending on its design features and its orbit, a satellite can acquire data at different spatial resolutions (from kilometers to centimeters) and different refreshing times (from every 15 days up to daily fresh data).
With satellites we can collect the response of vines and monitor their conditions during the entire growing season. Check the Saturnalia Evolution Index to know more.
There exist two main types of Earth Observation satellites, based on the different ranges of wavelengths they operate at: optical and radar. The former looks at the visible and infrared parts of the electromagnetic spectrum and generally needs an external source of radiation (typically, the Sun) to illuminate the observed objects. Cloud cover represents an issue for this type of sensor. Radars operate in the microwave spectrum; they are active sensors, in that they illuminate the target themselves and thus do not depend on the Sun. For this latter type of sensor, clouds do not represent an issue.
For the Saturnalia Evolution Index we take advantage of the Sentinel-2 and Landsat-8 optical constellations, and we are working to integrate multi-polarization radar data as well.
“Noise contribution” and “bias” are well-known problems in the signal processing community our founders grew in; being great at doing this is one of our competitive advantages, so unfortunately we cannot make the details public. Still, we know the problem well and we also have an effective, home-brewn solution. A range of solutions have been developed and published in textbooks and scientific papers. Moving to remotely sensed data, some of such solutions come under the technical term “unmixing”.
Satellites are complex devices that cost millions of dollars to design, launch and operate. Current missions are planned to last at least 10 years in orbit (historically, a few satellites exceeded 20 years in service). Moreover, the European Commission has already approved a new constellation that will guarantee constant monitoring of the Earth for at least 20 more years.
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